The present invention relates to the field of item and peer recommendation algorithms directed towards industry professionals. More specifically, the present invention is aimed at providing a more accurate and iterative recommendation algorithm that is adaptable based on a continuum of user actions or inputs rather than based on binary yes/no signals.
Prior art recommendation algorithms typically rely on a user profile and a set of filters for matching the user profile to an item or object (such as a document, a web page, a presentation, an article, a chart or graphic, a product, or the like) or a peer (such as an expert, a colleague in the same or similar industry, a service provider, or the like).
In item-to-item, item-to-user and user-to-user recommendation algorithms, one of the biggest challenges is to profile users accurately so that the right set of recommendations can be made. Traditionally recommendation engines have been able to use a user's declared or explicit profile and also the user's web site behavioral profile. The user's explicit profile includes the information he provides; for example, during a registration process, whereas the user's implicit profile may include the user's web site behavior such as the key words he or she searched on, articles he or she buys, articles he or she looks at, peer discussions he or she participates in, and the like. Such prior art recommendation engines are based on a user's set profile and simple binary scoring of the users actions indicating whether an item was viewed/not viewed or used/not used (e.g., in the form of a 1/0 or Y/N score), which records are then smoothed via weightings and/or time decay functions for use in the recommendation engine.
It would be advantageous to be able to adapt a user's profile iteratively based on the continuous spectrum of the user's interactions with different items, so that the recommendations can be more specifically tailored to a particular user. It would be particularly advantageous if such adaptation of the user's profile could be based on feedback obtained from a user, including feedback based on a user's interaction with a recommended item or direct input from a user as reflected in their modification of an item.
The methods, apparatus, and systems of the present invention provide the foregoing and other advantages.